Front-liners on the Sidelines: The credential recognition experiences of Filipino internationally-educated nurses (IENs) in Victoria, British Columbia (BC)
Bibliographic record
Abstract
The impacts of the nursing labour shortage are being felt across Canada but especially in Victoria, BC where place-based realities have impacted internationally-educated nurses’ (IEN) professional pursuits. Rising inflation, housing costs, and living expenses create challenging contexts for IENs from the Philippines who aim to settle, integrate and complete professional recertification processes in order to become registered nurses in BC. As provinces across the country vie for nurses to alleviate strains on the health care system, this study explores Filipino IENs’ integration experiences and settlement barriers. The study examines to what extent these factors might have influenced their educational upgrading, professional recertification, and workplace acculturation experiences. This exploratory study rooted in an interpretivist paradigm examines the experiences of nurses from the Philippines who recently migrated to Victoria in the last ten years. The key findings of the study posit that financial barriers, time barriers, deskilling, and mental health challenges are the most prevalent obstacles encountered by Filipino IENs in Victoria, BC. These findings are further expanded upon in order to understand the impacts that migration pathways, post- and pre-arrival immigration processes, familial responsibilities, English-language requirements, workplace discrimination and professional recertification pathways have on the complex integration and settlement experiences of Filipino IENs in Victoria, BC. Nine recommendations are proposed including the creation of more efficient migration pathways, investing in accessible information supports, prioritising effective communication, designing equitable policies that account for familial responsibilities, supporting flexible English language requirements, developing local navigational supports for IENs, addressing deskilling, adapting professional recertification pathways, and increasing collaboration between clinical practice programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".